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2011


Benchmark datasets for pose estimation and tracking
Benchmark datasets for pose estimation and tracking

Andriluka, M., Sigal, L., Black, M. J.

In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)

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publisher's site Project Page [BibTex]

2011


publisher's site Project Page [BibTex]


Steerable random fields for image restoration and inpainting
Steerable random fields for image restoration and inpainting

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.

ps

publisher site [BibTex]

publisher site [BibTex]

2010


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Nanofluidics of thin liquid films

Rauscher, M., Dietrich, S.

In Handbook of Nanophysics, Principles and Methods, 1, pages: 11-1-11-23, Handbook of Nanophysics, CRC Press, Boca Raton, 2010 (incollection)

icm

[BibTex]

2010


[BibTex]


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Dynamics of nanoscopic capillary waves

Mecke, K., Falk, K., Rauscher, M.

In Nonlinear Dynamics of Nanosystems, pages: 121-142, Wiley-VCH, Berlin, 2010 (incollection)

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DOI [BibTex]

DOI [BibTex]


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Locally weighted regression for control

Ting, J., Vijayakumar, S., Schaal, S.

In Encyclopedia of Machine Learning, pages: 613-624, (Editors: Sammut, C.;Webb, G. I.), Springer, 2010, clmc (inbook)

Abstract
This is article addresses two topics: learning control and locally weighted regression.

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link (url) [BibTex]

link (url) [BibTex]

2005


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The Boolean Model: from Matheron till today

Stoyan, D., Mecke, K.

In Space, Structure and Randomness: contributions in honor of Georges Matheron in the fields of geostatistics, random sets, and mathematical morphology, 183, pages: 151-182, Lecture Notes in Statistics, Springer, New York, 2005 (incollection)

icm

[BibTex]

2005


[BibTex]